The Curious Engineer

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Konrad Hinsen's blog

blog.khinsen.net

ScienceSoftware / engineering practiceEssays / personal

’10’15’20’25
Posts
95 from 22 Apr 2009 to 25 Sep 2026
Posting
monthly (6 in the last year)
Kind
personal blog
Feed
https://blog.khinsen.net/feeds/all.atom.xml

2026 5

  1. Knowledge refinement in the digital era
  2. Conviviality in computational science
  3. Cultures of making and relating
  4. Automating science
  5. Preparing for scientific deepfakes

2025 6

  1. Explorable explorable explanations
  2. Explaining software and computational methods
  3. Why computational reproducibility matters
  4. Why we should review research software
  5. Going for robustness: science
  6. Going for robustness

2024 1

  1. Modular malleability

2023 4

  1. The low-hanging fruit in computational reproducibility
  2. This blog gets a facelift
  3. Following branching conversations on Mastodon
  4. Deconstructing the Mastodon client

2022 1

  1. Welcome to my digital garden!

2021 2

  1. The dependency hubs in Open Source software
  2. The structure and interpretation of scientific models, part 2

2020 8

  1. The structure and interpretation of scientific models
  2. Some comments on AlphaFold
  3. The four possibilities of reproducible scientific computations
  4. The landscapes of digital scientific knowledge
  5. An open letter to software engineers criticizing Neil Ferguson's epidemics simulation code
  6. Wanted: a hierarchically modular software architecture
  7. Emacs as a malleable system
  8. The rise of community-owned monopolies

2019 6

  1. Pharo year one
  2. Industrialization of scientific software: a case study
  3. The industrialization of scientific research
  4. The computational notebook of the future (part 2)
  5. Is reproducibility good for scientific progress? (a paper review)
  6. The computational notebook of the future

2018 6

  1. Exploring Pharo
  2. Knowledge distillation in computer-aided research
  3. Literate computational science
  4. Scientific software is different from lab equipment
  5. Scientific communication is a research problem
  6. What can we do to check scientific computation more effectively?

2017 9

  1. Data science in ancient Greece
  2. Stability in the SciPy ecosystem: a summary of the discussion
  3. A plea for stability in the SciPy ecosystem
  4. There is no such thing as software development
  5. Why Python does so well in scientific computing
  6. Which mistakes do we actually make in scientific code?
  7. Reproducible research in the Python ecosystem: a reality check
  8. Reproducibility does not imply reproduction
  9. Sustainable software and reproducible research: dealing with software collapse

2016 3

  1. From reproducible to verifiable computer-aided research
  2. Composition is the root of all evil
  3. On HDF5 and the future of data management

2015 10

  1. From facts to narratives
  2. This blog is moving!
  3. The lifecycle of digital scientific knowledge
  4. A rant about software deployment in 2015
  5. Beyond Jupyter: what's in a notebook?
  6. The future of the Scientific Python ecosystem
  7. Another look at Julia
  8. The compartmentalization of knowledge
  9. Software in scientific research
  10. Why bitwise reproducibility matters

2014 6

  1. Drawing conclusions from empirical science
  2. The state of NumPy
  3. Reproducibility, replicability, and the two layers of computational science
  4. A first experience with Open Access publishing
  5. Exploring Racket
  6. The roles of computer programs in science

2013 6

  1. Python as a platform for reproducible research
  2. ActivePapers for Python
  3. Platforms for reproducible research
  4. Bye bye Address Book, welcome BBDB
  5. A critical view of altmetrics
  6. Lessons from sixteen years of molecular simulation in Python

2012 8

  1. Integrating scientific software and datasets into the citation record
  2. The ultimate calculator for Android and iOS
  3. The Nix package manager in computational science
  4. Unifying version control and dependency management for reproducible research
  5. Julia: a new language for scientific computing
  6. Binary operators in Python
  7. Python becomes a platform
  8. Teaching parallel computing in Python

2011 5

  1. A rant about mail clients
  2. EuroSciPy 2011
  3. Executable Papers
  4. Text input on mobile devices
  5. Bye bye iCal, welcome org-mode

2010 4

  1. The future of Python
  2. EuroSciPy 2010
  3. Science and free will
  4. Eclipse experiences

2009 5

  1. Scientific computing needs deterministic programming paradigms
  2. Sheldrake's New Science of Life
  3. Functional programming for scientific computing
  4. Static typing and code clutter
  5. Monads in Clojure